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dc.contributor.authorMata Carballeira, Oscar ORCID
dc.contributor.authorDíaz Rodríguez, Mikel
dc.contributor.authorDel Campo Hagelstrom, Inés Juliana ORCID
dc.contributor.authorMartínez González, María Victoria
dc.date.accessioned2020-10-15T08:16:25Z
dc.date.available2020-10-15T08:16:25Z
dc.date.issued2020-09-19
dc.identifier.citationApplied Sciences 10(18) : (2020) // Article ID 6549es_ES
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10810/46902
dc.description.abstractPollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people’s health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that cause elevated fuel consumption and emissions would achieve further reductions. For that reason, this work proposes a self-organized map (SOM)-based intelligent system in order to provide drivers with eco-driving-intended driving style (DS) recommendations. The development of the DS advisor uses driving data from the Uyanik instrumented car. The system classifies drivers regarding the underlying causes of non-optimal DSs from the eco-driving viewpoint. When compared with other solutions, the main advantage of this approach is the personalization of the recommendations that are provided to motorists, comprising the handling of the pedals and the gearbox, with potential improvements in both fuel consumption and emissions ranging from the 9.5% to the 31.5%, or even higher for drivers that are strongly engaged with the system. It was successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx ZynQ programmable system-on-a-chip (PSoC) family. This SOM-based system allows for real-time implementation, state-of-the-art timing performances, and low power consumption, which are suitable for developing advanced driving assistance systems (ADASs).es_ES
dc.description.sponsorshipThis work was supported in part by the Spanish AEI and European FEDER funds under Grant TEC2016-77618-R (AEI/FEDER, UE) and by the University of the Basque Country under Grant GIU18/122.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/TEC2016-77618-Res_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectadvanced driving assistance systems (ADAS)es_ES
dc.subjectADAS on-board vehicleses_ES
dc.subjectfuel consumptiones_ES
dc.subjecteco-drivinges_ES
dc.subjectdriving stylees_ES
dc.subjectmachine learning (ML)es_ES
dc.subjectunsupervised clusteringes_ES
dc.subjectself-organizing map (SOM)es_ES
dc.subjectfield-programmable gate array (FPGA)es_ES
dc.subjectprogrammable system-on-a-chip (PSoC)es_ES
dc.titleAn Intelligent System-on-a-Chip for a Real-Time Assessment of Fuel Consumption to Promote Eco-Drivinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2020-09-25T13:30:57Z
dc.rights.holder2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/10/18/6549es_ES
dc.identifier.doi10.3390/app10186549
dc.departamentoesElectricidad y electrónica
dc.departamentoeuElektrizitatea eta elektronika


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2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).